Previous research on rumour stance classification for tweets has been limited to the tweet as the unit to be classified. However, such approaches ignore the additional context and knowledge that can be gained from the structure of Twitter interactions within conversational threads [1]}, [2]}, [3]}. The latter are formed as Twitter users reply to one another's posts and ultimately users build on each others' stance towards the rumour, leading to a potential consensus. For example, a tweet may report a rumour (source tweet) and others may reply to it by further supporting it or providing counter-evidence. Our objective here is to mine the sequence of stance types encountered in conversational threads collected from Twitter. The ultimate goal could be to aggregate such views to help determine the veracity of a rumour, which we hypothesise our task could be helpful for.
